TY - JOUR

T1 - Distributions of forecasting errors of forecast combinations

T2 - implications for inventory management

AU - Barrow, Devon Kennard

AU - Kourentzes, Nikolaos

N1 - This is the author’s version of a work that was accepted for publication in International Journal of Production Economics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in International Journal of Production Economics, 177, 2016 DOI: 10.1016/j.ijpe.2016.03.017

PY - 2016/7

Y1 - 2016/7

N2 - Inventory control systems rely on accurate and robust forecasts of future demand to support decisions such as setting of safety stocks. Combining forecasts is shown to be effective not only in reducing forecast errors, but also in being less sensitive to limitations of a single model. Research on forecast combination has primarily focused on improving accuracy, largely ignoring the overall shape and distribution of forecast errors. Nonetheless, these are essential for managing the level of aversion to risk and uncertainty for companies. This study examines the forecast error distributions of base and combination forecasts and their implications for inventory performance. It explores whether forecast combinations transform the forecast error distribution towards desired properties for safety stock calculations, typically based on the assumption of normally distributed errors and unbiased forecasts. In addition, it considers the similarity between in- and out-of-sample characteristics of such errors and the impact of different lead times. The effects of established combination methods are explored empirically using a representative set of forecasting methods and a dataset of 229 weekly demand series from a household and personal care leading UK manufacturer. Findings suggest that forecast combinations make the in- and out-of-sample behaviour more consistent, requiring less safety stock on average than base forecasts. Furthermore we find that using in-sample empirical error distributions of combined forecasts approximates well the out-of-sample ones, in contrast to base forecasts.

AB - Inventory control systems rely on accurate and robust forecasts of future demand to support decisions such as setting of safety stocks. Combining forecasts is shown to be effective not only in reducing forecast errors, but also in being less sensitive to limitations of a single model. Research on forecast combination has primarily focused on improving accuracy, largely ignoring the overall shape and distribution of forecast errors. Nonetheless, these are essential for managing the level of aversion to risk and uncertainty for companies. This study examines the forecast error distributions of base and combination forecasts and their implications for inventory performance. It explores whether forecast combinations transform the forecast error distribution towards desired properties for safety stock calculations, typically based on the assumption of normally distributed errors and unbiased forecasts. In addition, it considers the similarity between in- and out-of-sample characteristics of such errors and the impact of different lead times. The effects of established combination methods are explored empirically using a representative set of forecasting methods and a dataset of 229 weekly demand series from a household and personal care leading UK manufacturer. Findings suggest that forecast combinations make the in- and out-of-sample behaviour more consistent, requiring less safety stock on average than base forecasts. Furthermore we find that using in-sample empirical error distributions of combined forecasts approximates well the out-of-sample ones, in contrast to base forecasts.

KW - Time Series

KW - Forecasting

KW - Combination

KW - Inventory

KW - Safety Stock

U2 - 10.1016/j.ijpe.2016.03.017

DO - 10.1016/j.ijpe.2016.03.017

M3 - Journal article

VL - 177

SP - 24

EP - 33

JO - International Journal of Production Economics

JF - International Journal of Production Economics

SN - 0925-5273

ER -